Uppsala Monitoring Centre, Bredgränd 7B, 753 20, Uppsala, Sweden.
Centre for Evidence-Based Medicine, Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Road, Oxford, OX2 6GG, United Kingdom.
Syst Rev. 2020 Aug 13;9(1):180. doi: 10.1186/s13643-020-01429-z.
Signals of adverse drug reactions (ADRs) form the basis of some regulatory risk-minimization actions in pharmacovigilance. Reviews of limited scope have highlighted that such signals are mostly supported by reports of ADRs or multiple types of evidence. The time that elapses between a report of a suspected ADR and the communication of a signal has not been systematically characterized. Neither has the features of reports of suspected ADRs that authors used to support putative causal relationships, although difficulties with establishing causal relationships between medicinal products and adverse events have been highlighted. The objectives of this study will be to describe the evidence underpinning signals in pharmacovigilance, the features of reports of ADRs supporting signals, and the time that it takes to communicate a signal.
We shall retrieve records from PubMed, EMBASE, Web of Science, and PsycINFO (from inception onwards), without language/design restrictions, and apply backward citation screening. We shall hand-search the websites of 35 regulatory agencies/authorities, restricted publications from the Uppsala Monitoring Centre, and drug bulletins. Signals will be requested from the competent stakeholder, if absent from websites. We shall use VigiBase, the World Health Organization's Global Individual Case Safety Report database, to determine the dates on which ADRs were reported. We shall manage records using EndNote (v. 8.2); one reviewer will screen titles/abstracts and full texts, a second will cross-validate the findings, and a third will arbitrate disagreements. Data will be charted via the Systematic Reviews Data Repository, following the same procedures as for data retrieval. Evidence will be categorized according to the Oxford Centre for Evidence-Based Medicine Levels of Evidence. Features of reports of ADRs will be coded. Tables will display frequencies of types of evidence and features of reports of ADRs. We shall use plots or pictograms (if appropriate) to represent the time from the first report of a suspected ADR to a signal.
We expect the findings from this review will allow a better understanding of global patterns of similarities or differences in terms of supporting evidence and timing of communications and identify relevant research questions for future systematic reviews. SYSTEMATIC REVIEW REGISTRATION: osf.io/a4xns.
药物不良反应（ADR）信号是药物警戒中一些降低风险的监管措施的基础。有限范围的综述强调，此类信号主要基于 ADR 报告或多种类型的证据。从疑似 ADR 报告到信号传递之间的时间尚未系统描述。作者用来支持潜在因果关系的疑似 ADR 报告的特征也没有描述，尽管已经强调了在药品和不良事件之间建立因果关系的困难。本研究的目的将是描述药物警戒中信号的证据基础、支持信号的 ADR 报告的特征以及传递信号所需的时间。
我们将从 PubMed、EMBASE、Web of Science 和 PsycINFO（从开始时起）中检索记录，不限制语言/设计，并应用回溯引文筛选。我们将手检 35 个监管机构/当局的网站、乌普萨拉监测中心的限制出版物和药物公报。如果网站上没有信号，我们将向相关利益攸关方请求信号。我们将使用 VigiBase，即世界卫生组织的全球个体病例安全报告数据库，来确定 ADR 报告的日期。我们将使用 EndNote（v.8.2）管理记录；一位审查员将筛选标题/摘要和全文，第二位审查员将交叉验证结果，第三位审查员将仲裁分歧。数据将通过系统评价数据存储库进行图表绘制，采用与数据检索相同的程序。证据将根据牛津循证医学中心证据水平进行分类。ADR 报告的特征将进行编码。表格将显示证据类型和 ADR 报告特征的频率。我们将使用图表或象形图（如果合适）来表示从疑似 ADR 报告到信号的时间。
我们预计这项审查的结果将使人们更好地了解在支持证据和通信时间方面的全球相似性或差异模式，并确定未来系统评价的相关研究问题。
osf.io/a4xns。
